By fixing a stable clustering algorithm, we can generate arbitrarily large ensembles by applying different transformations on the embeddings. Random projections were successfully used in Consensus Clustering previously [1]}. By generating ensembles using random projections, we have control over the amount of diversity we can induce into the framework, by varying the dimension of the random projection. In addition to Random Projections, we also used diagonal transformations [2]} where different components of the representation vector are scaled differently. Hsu et al., [2]} illustrate that such scaling enables a diverse set of clusterings which is helpful for their meta learning task. We study the ablations over number of transformations needed and dimension of these transformations in the section .
